Question 1

What is quantitative stock analysis?

Accepted Answer

Quantitative stock analysis uses mathematical models, statistical methods, and machine learning algorithms to evaluate securities. StoQuant combines 93 features including technical indicators, fundamental ratios, sentiment signals, and macro data to generate alpha scores for 822 stocks.

Question 2

How does the hidden gem screener work?

Accepted Answer

The hidden gem screener uses the Benjamin Graham intrinsic value formula (V = EPS x (8.5 + 2g)) to identify undervalued small-cap stocks. It filters for P/E under 15, P/B under 1.5, PEG under 1.0, debt-to-equity under 0.5, and flags stocks trading at least 30% below intrinsic value with fewer than 8 analyst coverage.

Question 3

What machine learning models power the predictions?

Accepted Answer

StoQuant uses a 3-model stacking ensemble: LightGBM, CatBoost, and XGBoost with a logistic regression meta-learner. Predictions are calibrated with Venn-ABERS conformal prediction. The system uses 93 features spanning technical, fundamental, sentiment, macro, and alternative data signals.

Question 4

How does portfolio optimization work?

Accepted Answer

StoQuant integrates ML price forecasts with the Black-Litterman portfolio optimization model. ML predictions serve as investor views (Q vector) with model confidence intervals mapped to uncertainty (omega matrix). This produces optimal portfolio allocations that blend market equilibrium with AI-driven insights.

Question 5

Is my portfolio data secure?

Accepted Answer

Yes. StoQuant uses JWT authentication with timing-safe comparisons, rate limiting (300 req/min per IP), secure headers (HSTS, X-Frame-Options, X-Content-Type-Options), and portfolio ownership verification on every request. No sourcemaps are exposed in production.

Question 6

What data sources does StoQuant use?

Accepted Answer

StoQuant aggregates data from SEC EDGAR filings, FMP financial data, social media sentiment (Reddit, StockTwits, Twitter), Google Trends, insider trading records, institutional holdings, options flow, and macro economic indicators from FRED. All data is stored as append-only time series.

Question 7

Does StoQuant work with Claude Desktop or Claude Code?

Accepted Answer

Yes. StoQuant ships an official Model Context Protocol (MCP) server, stoquant-mcp, available on npm. One command — npx -y stoquant-mcp install — wires Claude Desktop, Claude Code, or any MCP-compatible client into roughly 30 read-only stock-research tools: prices, Q-Score, ML predictions, hidden-gem screener, insider trades, market regime, and macro dashboards. Power-tier subscription required.

PFSI (PFSI) — Quantitative Stock Analysis

What StoQuant measures for PFSI

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